designing-the-analysis
Use when you have an approved research question and need a concrete analysis plan, before touching outcome data or fitting any…
Use when receiving critical feedback on an analysis or manuscript, before implementing suggestions, especially if feedback seems unclear or methodologically questionable - requires verification, not performative agreement or blind changes
$ npx -y skills add K-Dense-AI/science-superpowers --skill receiving-critical-review --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/receiving-critical-reviewContext preview
The summary Claude sees to decide when to auto-load this skill.
Use when receiving critical feedback on an analysis or manuscript, before implementing suggestions, especially if feedback seems unclear or methodologically questionable - requires verification, not performative agreement or blind changes
name: receiving-critical-review description: Use when receiving critical feedback on an analysis or manuscript, before implementing suggestions, especially if feedback seems unclear or methodologically questionable - requires verification, not performative agreement or blind changes
Critique of an analysis requires technical evaluation, not emotional performance.
**Core principle:** Verify before changing. Ask before assuming. Methodological correctness over social comfort.
A reviewer pointing at a confound is doing you a favor only if you check whether the confound is real. Reflexively agreeing — or reflexively defending — both skip the verification that makes review worthwhile.
WHEN receiving review feedback: 1. READ: The complete feedback without reacting 2. UNDERSTAND: Restate the methodological concern in your own words (or ask) 3. VERIFY: Check it against the data, code, and pre-registration 4. EVALUATE: Is it correct FOR THIS analysis? 5. RESPOND: Technical acknowledgment or reasoned pushback 6. ACT: One item at a time, re-running and re-verifying each
**NEVER:**
**INSTEAD:**
IF any item is unclear: STOP - do not change anything yet ASK for clarification WHY: Methodological items interact. Adjusting for the wrong confound because you misread the concern can bias the result further.
BEFORE changing anything: 1. Is it methodologically correct for THIS data and design? 2. Would the change itself introduce bias (e.g., adding a collider as a covariate)? 3. Is there a pre-registered reason the analysis is the way it is? 4. Does the reviewer have the full context (the pre-registration, the data structure)? IF the suggestion seems wrong: Push back with technical reasoning and evidence IF you can't verify: Say so: "I can't verify this without [X]. Should I [investigate/ask]?" IF it conflicts with the pre-registration: A change to the registered analysis is a deviation. It renders that analysis exploratory. Flag this explicitly before making the change.
A reviewer may suggest a "better" analysis. Before adopting it:
**How:** technical reasoning, not defensiveness. Show the diagnostic, the DAG, the pre-registration line, or the reproduced number.
When feedback IS correct:
✅ "Verified — site does confound this. Added it; estimate drops to 0.09 [−0.01, 0.19]. Now inconclusive." ✅ "Confirmed leakage: the scaler was fit before the split. Refit on train only; AUC falls to 0.71." ✅ [Just fix it, re-run, and show the new number] ❌ "You're absolutely right!" ❌ "Great catch!" ❌ "Thanks for spotting that!" ❌ ANY gratitude or praise performance
**Why no thanks:** the corrected, re-verified result shows you heard the feedback. State the fix and the new evidence.
If you pushed back and were wrong:
✅ "You were right — I checked and the assumption is violated. Re-running with the robust estimator." ❌ Long apology or defense of why you pushed back
| Mistake | Fix | |---------|-----| | Performative agreement | State the concern or verify | | Blind change | Check against data/code/prereg first | | Batch edits without re-running | One at a time, re-verify each | | Assuming the reviewer is right | Check whether the change biases the result | | Silently changing the registered analysis | Flag it as a deviation → exploratory | | Avoiding pushback | Methodological correctness > comfort |
External feedback = hypotheses to verify, not orders to follow.
Verify against the data and the pre-registration. Question. Then act — and re-verify with `science-superpowers:verifying-results-before-claiming`.
Science Superpowers is a complete computational-science methodology for your research agents, built on a set of composable skills plus initial instructions that make sure your agent actually uses them.
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